This module takes, as input, the concatenation of user request \(Q\)  and its dialog history \(\mathcal {H}\) , which consists of the last clarifying question and user answer, and determine if clarification is needed. The module is implemented using a pre-trained language model, ELECTRA [1]}, which is fine-tuned on training data with clarification labels. Following the single prediction formulation of Figure REF  (Left), the model predicts the clarification label \(y \in \lbrace 1,0\rbrace \)  as
\(P_r(y|Q, \mathcal {H}) = \mathrm {softmax} (\mathrm {FC}(\mathbf {x}; \theta _1))\) ,
where \(\mathbf {x}\)  is the representation of the input produced by ELECTRA, and \(\mathrm {FC}(;\theta _1)\)  is the task-specific head implemented as a 2-layer fully-connected feed-forward network and parameterized by \(\theta _1\) .
